A multiresolution approach for the coding of edges of still images using adaptive arithmetic coding

International audienceAn edge coding scheme based on chain code representation in a multiresolution image coding context is presented. Our method enhances the coding schemes that describe the source structure with Markov models, by using also an a priori knowledge from the previous decoded resolution images. Experiments using adaptive arithmetic coding have shown up to a 5% improvement for the bitrate compared to a Markovian scheme

Geometry Compression of 3D Static Point Clouds based on TSPLVQ

International audienceIn this paper, we address the challenging problem of the 3D point cloud compression required to ensure efficient transmission and storage. We introduce a new hierarchical geometry representation based on adaptive Tree-Structured Point-Lattice Vector Quantization (TSPLVQ). This representation enables hierarchically structured 3D content that improves the compression performance for static point cloud. The novelty of the proposed scheme lies in adaptive selection of the optimal quantization scheme of the geometric information, that better leverage the intrinsic correlations in point cloud. Based on its adaptive and multiscale structure, two quantization schemes are dedicated to project recursively the 3D point clouds into a series of embedded truncated cubic lattices. At each step of the process, the optimal quantization scheme is selected according to a rate-distortion cost in order to achieve the best trade-off between coding rate and geometry distortion, such that the compression flexibility and performance can be greatly improved. Experimental results show the interest of the proposed multi-scale method for lossy compression of geometry

Livrable D1.2 of the PERSEE project : Perceptual-Modelling-Definition-of-the-Models

Livrable D1.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.2 du projet. Son titre : Perceptual-Modelling-Definition-of-the-Model

Estimation of Perceptual Redundancies of HEVC Encoded Dynamic Textures

International audienceStatistical redundancies have been the dominant target in the image/video compression standards. Perceptually, there exists further redundancies that can be removed to further enhance the compression efficiency. In this paper, we considered short term homogeneous patches that fall into the foveal vision as dynamic textures, for which a psychophysical test was used to estimate their amount of perceptual redundancies. We demonstrated the possible rate saving by utilizing these redundancies. We further designed a learning model that can precisely predict the amount of redundancies and accordingly proposed a generalized perceptual optimization framework

1D-mosaics grouping using lattice vector quantization for a video browsing application

International audience1D-mosaics have been introduced as a tool for structuring and navigation in video content. These objects can be con- sidered as the spatio-temporal signatures of the video shots. Our work aims at grouping automatically the video shots into scenes using these signatures. The original method is based on the tree-structured lattice vector quantization of the 1D-mosaics. Because of the hierarchical structure of the code-books, they can be compared progressively, and lattice use is time efficient. Indexing retrieval results are given for two video sequences, and different mosaics are successively compared to each other in order to assess the presented scheme's effectiveness

Livrable D1.2 of the PERSEE project : Perceptual-Modelling-Definition-of-the-Models

Livrable D1.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.2 du projet. Son titre : Perceptual-Modelling-Definition-of-the-Model

Flexible Storage of Still Images with a Perceptual Quality Criterion

International audienceThe purpose of the paper is to introduce a new method for flexible storage of still images. The complete design of the system is described with the scalable encoding, the distortion computation, the bits allocation strategy, and the method for the memory management. The main improvement is the full exploitation of a perceptual metric to assess precisely the introduced distortion when removing a layer in a scalable coding stream. Experimental results are given and compared with a system which uses the PSNR as distortion metric

Vector Quantization by Packing of Embedded Truncated Lattices

International audienceThe purpose of this paper is to introduce a new vector quantizer (VQ) for the compression of digital image sequences. Our approach unifies both efficient coding methods: a fast lattice encoding and an unbalanced tree-structured codebook design according to a distortion vs. rate tradeoff. This tree-structured lattice VQ (TSLVQ) is based on the hierarchical packing of embedded truncated lattices. So we investigate the design of the hierarchical set of truncated lattice structures which can be optimally embedded. We present the simple quantization procedure and describe the corresponding tree-structured codebook. Finally two unbalanced tree-structured codebook design algorithms based on the BFOS distortion vs. rate criterion are used

Perceptual modelling for 2D and 3D

Livrable D1.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.1 du projet

Etude d'algorithmes de quantification vectorielle arborescente pour la compression d'images fixes

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